| _version_ | 1866902085675515904 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Swarm intelligence optimization algorithms are widely used in engineering optimization and artificial intelligence fields due to their ability to solve complex nonlinear problems without requiring gradient information. However, existing algorithms generally face problems such as premature convergence, rapid loss of swarm diversity, and difficulty in dynamically balancing exploration and exploitation during the search process. To address these issues, this paper proposes a novel swarm intelligence optimization algorithm inspired by the rotational tracking behavior of dolphins during predation. By constructing a rotational search dynamics model, the algorithm describes the update process of candidate solutions as a rotational approximation motion around multiple potentially superior solutions. Furthermore, the algorithm introduces a fractional-order memory mechanism, chaotic phase driving, a self-learning sonar transition strategy, and a topology-based neighborhood cooperative search mechanism to enhance the stability and diversity of the search process from the perspective of dynamic systems and information regulation. This paper systematically describes the search mechanism of the algorithm from a mathematical modeling perspective and provides a theoretical analysis of its exploration and exploitation behavior. The proposed method offers a new approach to the modeling and analysis of swarm intelligence optimization algorithms</span>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18357998 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Rotating Dolphin Optimization Algorithm with Fractional-Order Memory and Chaos-Driven Multi-Center Search Zhang, Jincheng <p><span>Swarm intelligence optimization algorithms are widely used in engineering optimization and artificial intelligence fields due to their ability to solve complex nonlinear problems without requiring gradient information. However, existing algorithms generally face problems such as premature convergence, rapid loss of swarm diversity, and difficulty in dynamically balancing exploration and exploitation during the search process. To address these issues, this paper proposes a novel swarm intelligence optimization algorithm inspired by the rotational tracking behavior of dolphins during predation. By constructing a rotational search dynamics model, the algorithm describes the update process of candidate solutions as a rotational approximation motion around multiple potentially superior solutions. Furthermore, the algorithm introduces a fractional-order memory mechanism, chaotic phase driving, a self-learning sonar transition strategy, and a topology-based neighborhood cooperative search mechanism to enhance the stability and diversity of the search process from the perspective of dynamic systems and information regulation. This paper systematically describes the search mechanism of the algorithm from a mathematical modeling perspective and provides a theoretical analysis of its exploration and exploitation behavior. The proposed method offers a new approach to the modeling and analysis of swarm intelligence optimization algorithms</span>.</p> |
| title | A Rotating Dolphin Optimization Algorithm with Fractional-Order Memory and Chaos-Driven Multi-Center Search |
| url | https://doi.org/10.5281/zenodo.18357998 |